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Local monotone operator learning using non-monotone operators: MnM-MOL
Inspired by convex-non-convex regularization strategies, we now impose the monotone constraint on the sum of the gradient of the data term and the CNN block, rather than constrain the CNN itself to be a monotone operator.
Deep Image Prior using Stein's Unbiased Risk Estimator: SURE-DIP
The deep image prior (DIP) algorithm was introduced for single-shot image recovery, completely eliminating the need for training data.
ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms
We introduce a novel metric termed the ENsemble Stein's Unbiased Risk Estimate (ENSURE) framework, which can be used to train deep image reconstruction algorithms without fully sampled and noise-free images.
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